Tabular Classification
Scikit-learn
English
hierarchical
healthcare
ehr
copd
clinical-risk
tabular
scikit-learn
clustering
unsupervised
Instructions to use stormid/copd-model-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use stormid/copd-model-e with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("stormid/copd-model-e", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| import numpy as np | |
| def calc_ds_med(v): | |
| """ | |
| Calculate the median value of a subgroup by removing any float nulls and | |
| converting from days to integers | |
| -------- | |
| :param v: values in column | |
| :return: median value | |
| """ | |
| day = np.timedelta64(1, 'D') | |
| med_val = (v.dropna() / day).astype(int).median().astype(int) | |
| med_val *= day | |
| return med_val |